Principal Component Pursuit for Pattern Identification in Environmental Mixtures.

ABSTRACT:

Background

Environmental health researchers often aim to identify sources or behaviors that give rise to potentially harmful environmental exposures.

Objective

We adapted principal component pursuit (PCP)-a robust and well-established technique for dimensionality reduction in computer vision and signal processing-to identify patterns in environmental mixtures. PCP decomposes the exposure mixture into a low-rank matrix containing consistent patterns of exposure across pollutants and a sparse matrix isolating unique or extreme exposure events.

Methods

We adapted PCP to accommodate nonnegative data, missing data, and values below a given limit of detection (LOD). We simulated data to represent environmental mixtures of two sizes with increasing proportions ResultsPCP-LOD recovered the true number of patterns through cross-validation for all simulations; based on an a priori specified criterion, PCA recovered the true number of patterns in 32% of simulations. PCP-LOD achieved lower relative predictive error than PCA for all simulated data sets with up to 50% of the data DiscussionPCP-LOD serves as a useful tool to express multidimensional exposures as consistent patterns that, if found to be related to adverse health, are amenable to targeted public health messaging. https://doi.org/10.1289/EHP10479.

SUBMITTER: Gibson EA 

PROVIDER: S-EPMC9683097 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature